https://github.com/anderson101866/cualgo
A cross-platform Pytnon library for fundamental algorithm with GPU-accelerated computing
https://github.com/anderson101866/cualgo
algorithm cuda gpu gpu-acceleration gpu-computing numpy python
Last synced: 3 months ago
JSON representation
A cross-platform Pytnon library for fundamental algorithm with GPU-accelerated computing
- Host: GitHub
- URL: https://github.com/anderson101866/cualgo
- Owner: anderson101866
- License: mit
- Created: 2023-10-09T18:05:19.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-14T04:31:08.000Z (over 1 year ago)
- Last Synced: 2025-03-17T17:51:27.998Z (3 months ago)
- Topics: algorithm, cuda, gpu, gpu-acceleration, gpu-computing, numpy, python
- Language: Python
- Homepage:
- Size: 1.51 MB
- Stars: 26
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CuAlgo
CuAlgo is a Python library benefiting from GPU-accelerated computing, featuring a collection of fundamental algorithms implemented with CUDA. Currently, it includes the Floyd-Warshall algorithm for graph analysis, showcasing the potential of GPU acceleration.
[](https://repology.org/project/python:cualgo/versions) [](https://pypi.org/project/cualgo/) [](https://pypi.org/project/cualgo/) [](https://github.com/anderson101866/cualgo/actions/workflows/python-app.yml)
## Key Features
#### Graph Algorithms:
- Floyd-Warshall algorithm
## Why CuAlgo?
- **Significant Speedup**: Experience substantial performance gains with CuAlgo's GPU-accelerated algorithms compared to their CPU approaches.
- **User-Friendly Python Interface**: CuAlgo provides convenient interface for Python users. It is compatible with **NumPy**, allowing for easy data interchange with existing scientific computing workflows. Ensuring that python developers can leverage GPU acceleration without delving into CUDA programming details.
- **Cross-Platform Compatibility**: Developed with CMake, CuAlgo supports cross-platform development, enabling seamless compilation on various operating systems.
## Performance Evaluation
Explore the Floyd-Warshall implementation using different datasets of sizes N=40, N=1000, and N=2000. This section presents a comprehensive analysis of the efficiency improvements achieved through GPU acceleration.
### Methodology
- **CPU Version**: The algorithm is executed on the CPU without GPU acceleration.
- **CPU (12 threads) Version**: Runs on the CPU with 12 threads using OpenMP.
- **GPU (Unoptimized) Version**: Initial GPU implementation with similar parallelism as the next GPU (Optimized) Version.
- **GPU (Optimized) Version**: GPU implementation with optimizations, including loop/block unrolling, dynamic parallelism, and coalesced memory access, fully leveraging GPU resources efficiently.
The charts illustrate the speedup achieved by CuAlgo's GPU-accelerated algorithms over CPU-based implementations. Notably, the optimized GPU version outperforms both the unoptimized GPU and CPU versions when N grows large, emphasizing the impact of optimization on algorithm efficiency.
#### Hardware and Software Information:
| | |
|-----------|-------------------------------------|
| CPU | AMD Ryzen 9 5900X 12-Core Processor |
| GPU | NVIDIA GeForce RTX 3060 Ti - 8GB |
| RAM | 32GB DDR4 3600 Mhz |
| CUDA Toolkit Version | 12.2 |
| GPU Driver Version | 537.13 |
## Prerequisites
(For linux, need GCC compiler with C++ support[^GCC_ONLY], and GNU make)
1. Latest [NVIDIA GPU driver](https://www.nvidia.com.tw/Download/index.aspx)
2. *Python 3.7+ with pip available*
3. *Latest CUDA toolkit installed with nvcc compiler. [(download here)](https://developer.nvidia.com/cuda-downloads)*
**NOTE: [Recommended]** You can skip 2 and 3. by using [conda](https://repo.anaconda.com/archive/), see [Installation](#Installation) below
## Installation
### Linux / Windows [Recommended]:
```bash
conda install cuda -c nvidia
python -m pip install --upgrade pip setuptools
pip install cualgo
```
### Windows (without conda):
1. Install NVIDIA latest GPU driver by yourself
2. `python -m pip install --upgrade pip setuptools && pip install cualgo`
## Sample Code
Support data type of `Numpy`.
```python
from cualgo import graph as cg
import numpy as np
graph = np.array([
[0 , 7 , np.inf, 8],
[np.inf, 0 , 5 , np.inf],
[np.inf, np.inf, 0 , 2],
[np.inf, np.inf, np.inf, 0]
], dtype=np.float64)
print(cg.floydwarshall(graph))
# [[0.0, 7.0, 12.0, 8.0], [inf, 0.0, 5.0, 7.0], [inf, inf, 0.0, 2.0], [inf, inf, inf, 0.0]]
```
Or just simply pass 2D `list` in python
```python
from cualgo import graph as cg
INF = 9999
graph = [
[0 , 7 , INF, 8],
[INF, 0 , 5 , INF],
[INF, INF, 0 , 2],
[INF, INF, INF, 0]
]
print(cg.floydwarshall(graph))
# [[0, 7, 12, 8], [9999, 0, 5, 7], [9999, 9999, 0, 2], [9999, 9999, 9999, 0]]
```
[^GCC_ONLY]: GCC works more compatible with CUDA's compiler than clang